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LUNG CANCER OMICS. VLADIMIR LAZAR MD, PhD Director of IGR’s Genomic Centre and Integrated biology platform vladimir.lazar@igr.fr. 2 nd ,Quebec conference on Therapeutic Resistance in Cancer Montreal, November 6th, 2010. Cost in metastatic NSCLC. Lung cancer overview 170 000 cases in USA

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lung cancer omics
LUNG CANCER OMICS

VLADIMIR LAZAR MD, PhD

Director of IGR’s Genomic Centre and Integrated biology platform

vladimir.lazar@igr.fr

2nd ,Quebec conference on Therapeutic Resistance in Cancer

Montreal, November 6th, 2010

cost in metastatic nsclc
Cost in metastatic NSCLC
  • Lung cancer overview
  • 170 000 cases in USA
  • 380 000 cases in Europe
  • At diagnosis 70% are metastatic
  • Overal survival at 5 Years <15%

Erlotinib

>2 m.

COST-EFFECTIVEMESS

20

18

Pemetrex.

> 2 m.

16

Erlotinib

2 m.

Cetux.or bev. >2 m.

14

Pemetrex

2 m.

Erlotinib

2 m.

12

Bev./Cet

2 m.

Pemetrex

2 m.

Docetax.

2 m.

10

Overall survival (median)

8

Platinum

+ 3rd gen.

8–10 m.

Platinum

+ 3rd gen.

8–10 m.

Platinum

+ 3rd gen.

8–10 m.

Platinum

+ 3rd gen.

8–10 m.

Platinum

+ 3rd gen.

>10 m.

6

Cisplatin

‘old fashion’6–8 m.

4

BSC

2–4 m.

2

0

70’s

80s

90s

2000

2005

2007-8

2013

slide3

Improve therapy

Patients with

same diagnosis

Other treatments

Non responders

Toxicity

Responders

with standard therapy

Goals of tailoring therapy according to predictive markers

Gandara R, et al. J Clin Oncol, 2007: Abst 7500

slide4

30

Classic Strategy for biopsies collection / analysis

  • 1 biopsy per patient, before treatment
  • Cohort responders non responders
  • Corelate data with end point and

Tumor

10%

  • Noise linked to the wide interindividual variability
  • (genetic background, sexe, organ, tumor type….)
  • need of large sample size, >>100
  • (e.g MINDACT Clinical trial >6,000 patients)
        • Not compatible with limited number of patient.
  • List of gene obtained instable, not able to predict clinical benefit.

Genetic

Variability

90%

noise

Histologic

preparation

Michiels S, et al. Lancet. 2005 - Prediction of cancer outcome with microarrays: a multiple random validation strategy.

Michiels S, et al. Br. J. Cancer 2007 - Interpretation of Microarray Data

slide6

31

IGR sequential Biopsies program

« 2 biopsies , before/after treatment »

Tumoral versus normal tissue

  • Avoid inter-individual variability
  • (same patient, same genetic background, same tumour type…)
  • Advantage dual-fluorescence labeling
  • (direct comparison)

Drug

effect

on

Tumor

85%

  • Preliminary studies
  • 5 couples of biopsies analyzed in duplicate & dye-swap.
    • SD of log of l’exp° « before » et « after » (SD1= 1,6)
    • SD of log of l’exp° « before/after »(SD2=0,4)
  • =>sample size needed to detect the same difference with « t-test »
  • «  usual» Strategy n= 86
  • « Sequential Biopsies » Strategy n=5
  • Tumor versus normal = individual studies

noise

example igr s team project

Expression BEFORE

short name=CD69

short name=CD69

1000000

1000000

100000

100000

10000

10000

1000

1000

P= 0.8654

100

P= 0.0012

100

No response

Response

Response

No response

Ratio of expression BEFORE/AFTER

10.0

Expression Ratio

1.0

P=7.5E-12

0.1

No response

Response

32

Example IGR’s Team project

Expression AFTER

Mantle cell lymphoma

Sequential Biopsies

Proteasome inhibitor

slide8

SOP pain, anxiety and risk management

Radiologie-Interventionnelle team (Dr T De BAERE)

slide9

FNA

18 gauges

23 gauges

  • HISTOLOGIC control ON CYTO
  • Lysis buffer (DNA, RNA Proteins)
  • Highcontent in tumoral cells in breast tumors,
  • metastatic lymph nodes,lymphomas
  • Possible cell suspension

biopsies

18 gauges

Control radio/echo

  • SNAP FROZEN VS RNA LATTER
  • HISTOLOGY CONTROL
  • RISK OF MIRROR ADJACENT BIOPSY
  • Variable % of tumoralcells
  • Need suplementary QC
p4b p4t
P4B + P4T

Profils similaires, à la dynamique près (amplitude supérieure pour P4B).

slide14

6th PCRD

Coordinator Pr Johan Hanson Karolinska Instituted

IGR – Genomic workpackage

Chemores : the first fully integrated Omics project in Lung cancerperformed with dual biopsies strategies T versus normal tissue

comparisons
Comparisons

Each patient TUMOR VS normal Tissue,( certified by histology control>85%, unique quality)

  • Groups:
  • To compare
    • Group 1 vs 2 (prognostic + predictive)
    • Group 3 vs 4 (prognostic)
    • Interaction: (1-2) vs (3-4) = predictive biomarkers
  • To compare
    • Tumor versus normal in ADK and SCC (early diagnosis)
    • Individualized estimation of resistance and of sensitivity
slide16

Lung cancer overview

  • 170 000 cases in USA
  • 380 000 cases in Europe
  • At diagnosis 70% are metastatic
  • Overal survival at 5 Years <15%
  • Early diagnosis ( compare T vs normal lung tissues)
    • Serum biomarkers –target secreted proteins
    • Enhancing sensitivity of imaging –target receptors
  • Predict efficacy of treatments
    • Populational studies (dissociate prognostic and predictive biomarkers
    • Individualized selection of treatment
  • Switch to integrative medicine (P4 medicine)
molecular data
Molecular data
  • DNA
    • CGH (comparative genomic hybridization): measures copy number. Agilent 250K array
    • Methylation array: measures gene silencing. (Tumor-suppressor genes are often silenced.)
    • Full sequencing of candidate genes (1,000 genes)
  • RNA
    • Exon expression array. Agilent 244K array. Average 8 exons/gene.
    • microRNA. Affects mRNA-protein translation. Agilent array~800miRNA.
  • Protein
    • LC/MS method
clinical data
Clinical data
  • N=123 patients
  • table(Relapse, Adj.chemo)

Adj.chemo

Relapse 0 1

0 39 36

1 22 26

  • Pilot data: 4 subjects/group
  • Cisplatin + vinorelbin regimen
anova 3 vs 4
ANOVA 3 vs 4

P18_CHE_an34_244F_c2d_397.xls

slide35

Building of algorithme relies on 3 steps

9

Complet genome profiling of the Tumor (metastasis) as compared to the original histological normal tissue

Tumor

Normal

Cancer is a clonal disease Cancer is a polygenic disease Drivers are mutations

slide36

10

Second step

Identifiction of all genes altered by

The drugs, or interacting with drugs

Understanding of the interaction drug-gene ( genes of resistance, targets, genes of sensitisation,

slide38

11-01-10

Baseline

10-03-10

After 2 cycles

slide39

11-01-10

Baseline

10-03-10

After 2 cycles

slide40

11-01-10

Baseline

10-03-10

After 2 cycles

slide41

11-01-10

Baseline

10-03-10

After 2 cycles

male caucasian 58y 2003 nsclc ct4 n0 m1

2

Male Caucasian,58Y, 2003, NSCLC, cT4,N0,M1

  • 9 therapeutic linesCisplatin Gemzar TaxotereNavelbineTaxolCarboplatinMediastinal RadiotherapyIRESSA AlimtaTarceva(HKI 272 (included in clinical trial) (pan Her Inhibitor)
start hki272

4

START HKI272

Adrenal node (C2) = 26 mm

21 11 08 progression disease

Adrenal node (C2) = 58 Disease Progression

New sublclavious metastasis

7

21/11/08 : Progression Disease

DECISION TO STOP HKI 272

slide45

2

  • 9 therapeutic linesCisplatin (108)Gemzar (70)Taxotere (77)Navelbine (50)Taxol (82)Carboplatin (66)Mediastinal RadiotherapyIRESSA (66)Alimta (73)Tarceva(HKI 272 (included in clinical trial) (pan Her Inhibitor)
slide47

13

23/12/08 =

  • STOP HKI 272
  • START Lapatinib +Xeloda + Thiothepa (introduced sequentially during 1 month)
01 02 10 still on lapatinib xeloda thiotepa

16

01/02/10: Still on Lapatinib, Xeloda,Thiotepa

Stable Disease !!

Adrenal node (C2) = 62 mm

slide49

13

45 years

Rhabdomyosarcoma

5 metastases

slide52

Genomics platform based on Agilent technology

T7 RNA amplification

& Labeling

RNA isolation

AAAAA

A

AAAAA

AAAAA

QC

QC

B

AAAAA

AAAAA

AAAAA

AAAAA

QC

Q-PCR validations

Resolver™

QC

QC

Gene expression

microRNA

Hybridization

Sample

oligo Microarray

DNA isolation &

fragmentation

Klenow Labeling

CGH

ChIP

methylation

Scanning

Study design, Standard operating procedures, Quality control

Bioinformatics, biological interpretation of data

Bioinformatics

slide53

WIN

GAP

  • Worldwide Innovative Networking

Only 50 % cured

Late diagnosis

Therapeutic failure

slide54

WIN GOALS

  • Early Diagnosis
  • Predict efficacy of Individualized Treatments
  • Innovative drug associations

Focus

  • Validation of new tools, biomarkers, technologies in America Europe Asia and Middle East patients
  • Innovative clinical trials conducted worldwide
  • An operational structure; The WIN consortium
  • Dissemination of knowledge: The WIN symposium
  • WIN database

Strategy

  • First concrete results in 3-5 years (kit early diagnosis)
  • Critic mass in relationship with Pharma and Regulatories
  • Generate incoming revenues

Expected

results

Accelerate integration of ground-breaking personalized cancer medicine discoveries into clinical practice and to significantly improve clinical outcome and quality of life

george bernard shaw
George Bernard Shaw

You see things that happen and ask ”WHY”. I dream about things that did not happen and ask ”Why Not”